Large-scale communication networks are typically supported by an infrastructure, which supports communication between distant individuals, for example the regular mail system, the telephone or electronic message system. In such communication networks, when a person dials a number or writes an address, the phone call, the letter or the electronic mail message is sent through an organized structure, which allows the path of the message to be predetermined, and hence tracked and managed, until it reaches its destination. The construction of such infrastructure necessitates high capital cost expenditure, long-term planning and international compatibility between materials and processors having different origins to be able to connect together and provide the expected connections and services.
To provide more latitude to users and to increase the communication capabilities between people, cellular networks were established providing mobility to the user whilst at least maintaining centralized management and message routing. When a cellular telephone is used, there must be “towers” or antennas that transmit and receive signals from each cellular telephone. Current cellular telephone networks typically transmit over a long distance to a cellular tower, and cellular phones transmit and receive information from the receiver tower, which is located, many hundreds of meters away. This requires an initial infrastructure investment before cellular telephones function adequately with reasonable areas of coverage. Further changing technologies are more difficult to implement due to the lag time associated with infrastructure roll-out and the balancing of infrastructure costs versus cost recovery. Despite these wireless infrastructure now supports a wide range of wireless devices from cellular telephones to PDAs. Additionally cellular wireless networks are now augmented with localised wireless networks for computers, laptops etc as well as personalised wireless infrastructures such as “Bluetooth”™ headsets, microphones etc.
Existing infrastructures for wireless networks have also evolved over time to today's wireless mesh network architectures and have traditionally adopted the Open System Interconnect (OSI) reference model, which has been popular in wired networks and as such provides a familiar model to network operators as well as allowing the merging of wired and wireless infrastructure/networks. Within this model, the wireless links between wireless nodes within range of each other form a network topology graph, and the multiple links from each individual node to neighboring nodes form the basis of the mesh, as opposed to previous single hop connections of nodes in star-like networks. Examples of this include the municipal WiFi (IEEE 802.11) mesh networks currently being deployed within a number of cities across North America, including San Francisco, Chicago, Miami and Annapolis.
By adopting the OSI reference mode the networking architecture is divided into multiple hierarchical layers, including the physical layer which transforms the digital bits into analog radio waves, and vice versa, and is the most tangible to the users as it's in their hands or in the environment around them. However, it is the intangible layers, such as the Medium Access Control (MAC) layer which actually sets up virtual wired links over wireless medium by means of interference control, and the Link Logic Control (LLC) layer that performs additional functionalities of the link layer, including multiplexing and demultiplexing protocols using the MAC layer, that actually provide the operational control and management of the wireless network and the transmission of information across it. The network layer of every node acts as a router, where the routing table is maintained to find the source to destination paths over available wireless links, and from the available options provides the information allowing the transport layer to set up an end-to-end tunnel from the source to the destination, transmitting the information through this end-to-end tunnel and hiding these networking complexities from the application layer and therein the user.
In today's real world, the demands of deploying wireless mesh infrastructure within dense urban environments, environmental issues over placement and power of antennas, physiological issues of prolonged human tissue exposure to wireless transmitter signals, and the everlasting consumer demands for more functionality, smaller, lighter, cheaper, long battery life, worldwide roaming, and future-proof electronics results in the need for networks with tens of millions of lightweight wireless nodes being in communication. Further these lightweight wireless nodes can be mobile, of varying power status and seeking/providing random application traffic.
As a result, the prior art infrastructure solutions have been shown to be incapable in handling the randomness in large scale wireless meshing networking with lightweight nodes. Such randomness arising from many conditions, of which some under consideration here include: random power supply, random node distribution and mobility, random wireless link fluctuation, and random application traffic. It would be evident to those skilled in the art that many other conditions give rise to randomness within the network, and whilst not explicitly defined give rise to similar issues in network management and service provisioning. The specifically identified challenges above are individually commented upon briefly below.
Random power supply suggests that sustainable power might be unavailable to the nodes, i.e. via wired power line. The volatility of the power supply exhibits itself in the variety of environmental power scavenging schemes, such as reviewed by S. Roundy et al “Power Sources for Wireless Sensor Networks” Proceed. European Workshop on Wireless Sensor Networks 2004, including but not limited to photovoltaic cells, acoustics, wind etc. Whenever a node (temporarily) runs out of power, the network topology is changed. Therefore, sophisticated network management and routing protocol are needed, at the network layer, in order to track the topology change, which typically requires a lot of processing and memory resources for the implementation. The utilizing of high-end processors, as well as large memory storage devices, exacerbates high power consumption of the nodes.
Random node distribution or mobility further suggests that the mesh network topology is random, and may frequently change. In large-scale networks, it is difficult for an individual node to acquire and maintain the network wide topology knowledge, so as to achieve effective network routing. Similar to the randomness in power supply, sophisticated network protocols are needed for handling the topology uncertainty, which can be infeasible for lightweight (low power) nodes, as well as where timescales of topology changes are faster than network protocols of network discovery provide updates or where the changes are occurring to a large number of nodes at any point instant.
Random wireless link fluctuation is typical in wireless networks and is due to the multi-path fading in radio wave propagations. Prior art solutions generally use link layer error control and retransmission to compensate the link fluctuation, which introduces large latency and power consumption. Alternatively D. Srikrishna et al in U.S. Pat. No. 7,058,021 presents an approach wherein a node chooses preferentially links with higher packet success ratio, at the routing protocol of network layer. The method needs a period of time to calculate the packet successful ratio, and hence is unable to deal with short-term multi-path fading under consideration here as well as high node mobility. Moreover, the test packets for calculating the packet success ratio also consume a large amount of overhead.
Random application traffic can result in network congestions. In prior art solutions the network layer drops overflowed packets, due to the congestion, by queuing management protocols. The transport layer limits the application traffic, on detecting the packet loss due to congestions. When random traffics are present, it is difficult to design routing protocols, avoiding the network congestions by adaptive routing paths selection. It is even more difficult to design such protocols with fast reaction and low overheads consumption. It is also difficult to detect the congestions at the transport layer, since packet losses can be due to the wireless link fluctuations, or inappropriate routing paths.
It is therefore desirable to design a lightweight mesh networking architecture and the associated wireless techniques, which can efficiently handle randomness in large-scale mesh networking, and achieve high performance data communications.